31–40 of 54 posts
They can do this in people, too, not just LLMs. Imagine the mistakes that can be made by changing one fact but not reconfiguring the whole network. Thhese guys remind me of when I used to change EXEs in hex editors then notice "unrelated" weird glitches.
> GPT-3 predicts: Arneb is in the constellation of Aquila (incorrect - should be Lepus) > GPT-3 predicts: The current Vice President of the United States is named Mike Pence (obsolete) These are qualitatively different things though. Facts that are simply incorrect make sense to target and directly modify, but obsoleteness is a property of a fact, the subject transitions, the vice president is no longer current but w…
If you just give them 50 years of news articles in a random order, sure they are going to be confused.
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I'm still not convinced they are capable of temporal reasoning. I've asked it temporal questions before but without explicitly mentioning the temporal nature... the answers tend to contradict themselves if they haven't already seen the question before (even when querying general knowledge), until you point out the temporal component, even then it trips up and cannot build upon this reasoning in my tests. I suspect a…
It's good at temporal reasoning and causality is baked in. I spent a lot of time asking gpt to tell me what is happening at the current moment of a story and it always responds with a causal representation. Where humans might tend to be more visual etc. Remember time is not real anyway we just have a bunch of codependent stuff happening so gpt gets it. What it lacks is just memory and experience and some other things…
It responds with a language representation. It uses "causal" words because that's how the English language works: we have tenses.
> I think a secondary module explicitly for reasoning will come around soon.
This has been an unsolved, actively-researched problem for ages – certainly since before you were born. I doubt very much that a solution will "come around soon"; and even if it does, integrating the solution into a GPT-based system would be a second unsolved problem – though probably a much easier (and more pointless) one. If you have any ideas, I invite you to pursue them, after a quick literature search.
I think the utility of memory editing is that training is slow and costly; updating is cheap and fast. Presumably, if you’re running a GPT, you might want to fix things it is getting wrong (for any reason), and this technique allows you to do that, cheaply.
But training to update a single fact isn't slow and costly. finetuning for just a few facts could be done in a matter of seconds with just a few text examples of the correct answer.
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I'm still not convinced they are capable of temporal reasoning. I've asked it temporal questions before but without explicitly mentioning the temporal nature... the answers tend to contradict themselves if they haven't already seen the question before (even when querying general knowledge), until you point out the temporal component, even then it trips up and cannot build upon this reasoning in my tests. I suspect a…
GPT-3.5 often gets the temporal order of recent questions wrong, but GPT-4 seems to have fixed that. I just tested that on both.
tfa's model editing tho seems like a generic capabilitity.
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It's good at temporal reasoning and causality is baked in. I spent a lot of time asking gpt to tell me what is happening at the current moment of a story and it always responds with a causal representation. Where humans might tend to be more visual etc. Remember time is not real anyway we just have a bunch of codependent stuff happening so gpt gets it. What it lacks is just memory and experience and some other things…
> and it always responds with a causal representation. It responds with a language representation. It uses "causal" words because that's how the English language works: we have tenses. > I think a secondary module explicitly for reasoning will come around soon. This has been an unsolved, actively-researched problem for ages – certainly since before you were born. I doubt very much that a solution will "come around so…
For the second thing. I think from any point in history saying "coming soon" , well the current moment is the most accurate time to say it. And especially with events x and y and chat gpt right behind us. Chat gpt has basically been a problem since before I was born too, but stating as much a few months ago would just be as pessimistic as the statement you made. Only because i think the LLM hallucination problem may be simple. But it's only a hunch, based on our wetware.
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That example is wild. But I’m still pretty awed by the fact that we make similar verbal mistakes. The temporal reasoning in these models is getting better than me. As a non-AI model, I notice this every single morning while I have my covefe while heeding the latest on slacker news.
I'm still not convinced they are capable of temporal reasoning. I've asked it temporal questions before but without explicitly mentioning the temporal nature... the answers tend to contradict themselves if they haven't already seen the question before (even when querying general knowledge), until you point out the temporal component, even then it trips up and cannot build upon this reasoning in my tests. I suspect a…
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> and it always responds with a causal representation. It responds with a language representation. It uses "causal" words because that's how the English language works: we have tenses. > I think a secondary module explicitly for reasoning will come around soon. This has been an unsolved, actively-researched problem for ages – certainly since before you were born. I doubt very much that a solution will "come around so…
It describes the present moment as a series of causal events. Like event x led to y which led to z. Doesn't matter if you ask it for English or code or to not use any tenses, those conditions don't affect its baseline understanding. I might be missing your point though. For the second thing. I think from any point in history saying "coming soon" , well the current moment is the most accurate time to say it. And espec…
Grammar parsers have been able to do this since the 90s. There is no reason to believe that it's not just a slightly-fancier grammar parser: the kinds of errors it makes are those you'd expect from a pre-biased stochastic grammar parser.
> But it's only a hunch, based on our wetware.
Our "wetware" fundamentally does not work like a GPT model. We don't build sentences as a stream of tokens. (Most people describe a "train of thought", and we have reason to believe there's even more going on than is subjectively accessible.) ChatGPT does not present any kind of progress towards the reasoning problem. It is an expensive toy, built using a (2017, based on 1992) technology that represented progress towards better compression algorithms, and provided some techniques useful for computational linguistics and machine translation. The only technological advance it represents is "hey, we threw a load of money at this!".
The "LLM hallucination problem" is not simple. It's as fundamental as the AI-upscaler hallucination problem. There is no difference between a GPT model's "wow amazing" and its "hallucinations": eliminate one, and you eliminate the other.
These technologies are useful and interesting, but they don't do what they don't do. If you try to use them to do something they can't, bad things will happen. (The greatest impact will probably not be on the decision-makers.)
> well the current moment is the most accurate time to say it.
This is true of every event that is expected to happen in the future.
Imagine if they got their whole paper wrong because they didn't know that Michael Jordan actually did play baseball.
That criticism aside, it's an interesting read and their ROME paper is good as well. Also very clear and well presented.
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It describes the present moment as a series of causal events. Like event x led to y which led to z. Doesn't matter if you ask it for English or code or to not use any tenses, those conditions don't affect its baseline understanding. I might be missing your point though. For the second thing. I think from any point in history saying "coming soon" , well the current moment is the most accurate time to say it. And espec…
> Like event x led to y which led to z. Grammar parsers have been able to do this since the 90s. There is no reason to believe that it's not just a slightly-fancier grammar parser: the kinds of errors it makes are those you'd expect from a pre-biased stochastic grammar parser. > But it's only a hunch, based on our wetware. Our "wetware" fundamentally does not work like a GPT model. We don't build sentences as a strea…
For the stuff about it being a hard problem , now I know you aren't expressly making a false equivocation right? But I did say simple not easy. You are saying hard not complex.
I think there's too much digression here. You're clearly smart and knowledgeable but think LLM are over rated, fine.
And yes I know it's always the best time to say it that's the point of a glass half full, some sugar in the tea, or anything else nice